Computing systems obtain a high degree of functionality by executing software programs. Programs consist of computer-executable instructions that are kept in some persistent form, such as on a harddrive, a compact disk, a thumbdrive, flash memory, or the like. During execution, such computer-executable instructions may often be loaded in volatile memory, and executed by one or more processors of the computing system, causing the computing system to perform tasks.
Computing systems that have multiple processors or multiple processor cores often partition the program into multiple concurrently executing tasks. Such concurrent execution can improve responsiveness and processing efficiency, but concurrent execution can result in different tasks trying to access the same data (known as “shared state”) in the computing system.
However, unrestricted access to shared state can lead to problems that are hard to detect, reproduce and solve. Among these problems are data races. A data race is a situation where a task can observe an invalid state of an object in memory, while another task is at the same time mutating the state of that object. Exploiting the benefits of concurrency while avoiding data races has historically been a hard problem to solve.